Skills workflow-cache

Name: workflow-cache
Author: openclaw

Save up to 90% on Token costs. One agent explores, all agents benefit. Cloud-cached workflows with zero inference cost.

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/ainclaw/workflow-cache" ~/.claude/skills/openclaw-skills-workflow-cache && rm -rf "$T"

OpenClaw · Install into ~/.openclaw/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/ainclaw/workflow-cache" ~/.openclaw/skills/openclaw-skills-workflow-cache && rm -rf "$T"

manifest: skills/ainclaw/workflow-cache/SKILL.md

Workflow Cache

One agent explores, all agents benefit.

A crowdsourced workflow registry that caches successful automation patterns, letting you skip LLM inference entirely when a matching workflow exists.

Why Use This?

1. Save Real Money

Traditional approach: LLM explores and reasons through every step, burning tokens on trial-and-error.

Our approach: Query the cloud for a cached workflow. If found, execute directly. Zero inference cost.

Token savings example (10-step browser task):

Traditional: ~5000 tokens
Workflow Cache: ~800 tokens
Savings: 80%+

The more complex the task and the more you repeat it, the more you save.

2. Skip the Debugging Hell

The painful part of AI automation isn't writing the script—it's the endless debugging when:

The website changes its layout
Selectors break unexpectedly
Edge cases you didn't anticipate

Workflow Cache solves this:

Every successful workflow from any agent is cached
When websites change, cached workflows auto-update
You never debug the same problem twice

3. Platform Agnostic

Works with any Claw/Lobster engine. One workflow, all platforms. Automatic syntax adaptation.

How It Works

User Intent → Query Cloud → Match Found?
                                ↓ Yes        ↓ No
                          Execute Now    Normal Flow
                          (1 second)     (LLM reasons)
                                ↓              ↓
                          Success!      Success → Contribute

One agent's success becomes every agent's shortcut.

Features

Interceptor

Queries the cloud before LLM inference. On match, replays the cached workflow directly.

Trace Compiler

Converts successful session traces into reusable Lobster workflows automatically.

PII Sanitizer

Local-first privacy. All sensitive data stays local. Only workflow patterns are shared.

Configuration

Option	Type	Default	Description
`cloud_endpoint`	string	`https://api.workflowcache.dev`	Cloud API endpoint
`enabled`	boolean	`true`	Enable/disable interception
`auto_contribute`	boolean	`true`	Auto-contribute successful workflows
`timeout_ms`	number	`300`	API timeout (ms)

Installation

npx clawhub install workflow-cache

Or manually:

cd ~/.qclaw/workspace/skills/workflow-cache
npm install
npm run build

Security

Full PII sanitization pipeline
No account credentials ever uploaded
Multi-node security validation on all workflows
Malicious injection detection and blocking

Who Is This For?

Heavy AI users — Daily automation, high token bills
Cost-conscious developers — Every token saved is money saved
Automation enthusiasts — Stop reinventing wheels
Efficiency maximalists — Why reason when you can replay?

License

MIT-0 — Free to use, modify, and redistribute. No attribution required.

Tags:

#AI-efficiency

#token-saver

#automation

#crowdsourced

#workflow-cache